The model is never the hard part.

The hard part is the data pipeline that feeds it, the evaluation harness that measures it, and the infrastructure that serves it.

Most AI projects don’t fail at training — they fail at everything else. The messy CSV someone emailed you. The label inconsistencies nobody noticed until month three. The latency spike when two requests hit at once.

Build boring, reliable infrastructure around your model. The model will surprise you less than the pipes.